LECTURE 1: ubiquity ; INTRODUCTION interconnection ; intelligence - - PDF document

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LECTURE 1: INTRODUCTION

Multiagent Systems Based on “An Introduction to MultiAgent Systems” by Michael Wooldridge, John Wiley & Sons, 2002.

http://www.csc.liv.ac.uk/˜mjw/pubs/imas/

Overview

Five ongoing trends have marked the history

• f computing:

ubiquity; interconnection; intelligence; delegation; and human-orientation

Ubiquity

The continual reduction in cost of computing

capability has made it possible to introduce processing power into places and devices that would have once been uneconomic

As processing capability spreads,

sophistication (and intelligence of a sort) becomes ubiquitous

What could benefit from having a processor

embedded in it…?

Interconnection

Computer systems today no longer stand

alone, but are networked into large distributed systems

The internet is an obvious example, but

networking is spreading its ever-growing tentacles…

Since distributed and concurrent systems

have become the norm, some researchers are putting forward theoretical models that portray computing as primarily a process of interaction

Intelligence

The complexity of tasks that we are capable

• f automating and delegating to computers

has grown steadily

If you don’t feel comfortable with this

definition of “intelligence”, it’s probably because you are a human

Delegation

Computers are doing more for us – without

• ur intervention

We are giving control to computers, even in

safety critical tasks

One example: fly-by-wire aircraft, where the

machine’s judgment may be trusted more than an experienced pilot

Next on the agenda: fly-by-wire cars,

intelligent braking systems, cruise control that maintains distance from car in front…

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Human Orientation

The movement away from machine-oriented

views of programming toward concepts and metaphors that more closely reflect the way we ourselves understand the world

Programmers (and users!) relate to the

machine differently

Programmers conceptualize and implement

software in terms of higher-level – more human-oriented – abstractions

Programming progression…

Programming has progressed through:

machine code; assembly language; machine-independent programming languages; sub-routines; procedures & functions; abstract data types;

• bjects;

to agents.

Global Computing

What techniques might be needed to deal

with systems composed of 1010 processors?

Don’t be deterred by its seeming to be

“science fiction”

Hundreds of millions of people connected by

email once seemed to be “science fiction”…

Let’s assume that current software

development models can’t handle this…

Where does it bring us?

Delegation and Intelligence imply the need to

build computer systems that can act effectively on our behalf

This implies:

The ability of computer systems to act

independently

The ability of computer systems to act in a way

that represents our best interests while interacting with other humans or systems

Interconnection and Distribution

Interconnection and Distribution have

become core motifs in Computer Science

But Interconnection and Distribution, coupled

with the need for systems to represent our best interests, implies systems that can cooperate and reach agreements (or even compete) with other systems that have different interests (much as we do with other people)

So Computer Science expands…

These issues were not studied in Computer

Science until recently

All of these trends have led to the emergence

• f a new field in Computer Science:

multiagent systems

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Agents, a Definition

An agent is a computer system that is

capable of independent action on behalf of its user or owner (figuring out what needs to be done to satisfy design objectives, rather than constantly being told)

Multiagent Systems, a Definition

A multiagent system is one that consists

• f a number of agents, which interact with
• ne-another

In the most general case, agents will be

acting on behalf of users with different goals and motivations

To successfully interact, they will require

the ability to cooperate, coordinate, and negotiate with each other, much as people do

Agent Design, Society Design

The course covers two key problems:

How do we build agents capable of independent,

autonomous action, so that they can successfully carry

• ut tasks we delegate to them?

How do we build agents that are capable of interacting

(cooperating, coordinating, negotiating) with other agents in order to successfully carry out those delegated tasks, especially when the other agents cannot be assumed to share the same interests/goals?

The first problem is agent design, the second is

society design (micro/macro)

Multiagent Systems

In Multiagent Systems, we address questions

such as:

How can cooperation emerge in societies of self-

interested agents?

What kinds of languages can agents use to

communicate?

How can self-interested agents recognize conflict,

and how can they (nevertheless) reach agreement?

How can autonomous agents coordinate their

activities so as to cooperatively achieve goals?

Multiagent Systems

While these questions are all addressed

in part by other disciplines (notably economics and social sciences), what makes the multiagent systems field unique is that it emphasizes that the agents in question are computational, information processing entities.

The Vision Thing

It’s easiest to understand the field of multiagent

systems if you understand researchers’ vision of the future

Fortunately, different researchers have different

visions

The amalgamation of these visions (and

research directions, and methodologies, and interests, and…) define the field

But the field’s researchers clearly have enough

in common to consider each other’s work relevant to their own

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Spacecraft Control

When a space probe makes its long flight from Earth

to the outer planets, a ground crew is usually required to continually track its progress, and decide how to deal with unexpected eventualities. This is costly and, if decisions are required quickly, it is simply not practicable. For these reasons,

• rganizations like NASA are seriously investigating

the possibility of making probes more autonomous — giving them richer decision making capabilities and responsibilities.

This is not fiction: NASA’s DS1 has done it!

Deep Space 1

http://nmp.jpl.nasa.gov/ds1/ “Deep Space 1

launched from Cape Canaveral on October 24,

• 1998. During a highly

successful primary mission, it tested 12 advanced, high-risk technologies in

• space. In an extremely successful extended

mission, it encountered comet Borrelly and returned the best images and other science data ever from a comet. During its fully successful hyperextended mission, it conducted further technology tests. The spacecraft was retired on December 18, 2001.” – NASA Web site

Autonomous Agents for specialized tasks

The DS1 example is one of a generic class Agents (and their physical instantiation in

robots) have a role to play in high-risk situations, unsuitable or impossible for humans

The degree of autonomy will differ depending

• n the situation (remote human control may

be an alternative, but not always)

Air Traffic Control

“A key air-traffic control system…suddenly

fails, leaving flights in the vicinity of the airport with no air-traffic control support. Fortunately, autonomous air-traffic control systems in nearby airports recognize the failure of their peer, and cooperate to track and deal with all affected flights.”

Systems taking the initiative when necessary Agents cooperating to solve problems beyond

the capabilities of any individual agent

Internet Agents

Searching the Internet for the answer to a

specific query can be a long and tedious

• process. So, why not allow a computer program

— an agent — do searches for us? The agent would typically be given a query that would require synthesizing pieces of information from various different Internet information sources. Failure would occur when a particular resource was unavailable, (perhaps due to network failure), or where results could not be obtained.

What if the agents become better?

Internet agents need not simply search They can plan, arrange, buy, negotiate –

carry out arrangements of all sorts that would normally be done by their human user

As more can be done electronically, software

agents theoretically have more access to systems that affect the real-world

But new research problems arise just as

quickly…

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Research Issues

How do you state your preferences to your agent? How can your agent compare different deals from

different vendors? What if there are many different parameters?

What algorithms can your agent use to negotiate

with other agents (to make sure you get a good deal)?

These issues aren’t frivolous – automated

procurement could be used massively by (for example) government agencies

The Trading Agents Competition…

Multiagent Systems is Interdisciplinary

The field of Multiagent Systems is influenced and

inspired by many other fields:

Economics Philosophy Game Theory Logic Ecology Social Sciences

This can be both a strength (infusing well-founded

methodologies into the field) and a weakness (there are many different views as to what the field is about)

This has analogies with artificial intelligence itself

Some Views of the Field

Agents as a paradigm for software engineering:

Software engineers have derived a progressively better understanding of the characteristics of complexity in software. It is now widely recognized that interaction is probably the most important single characteristic of complex software

Over the last two decades, a major Computer

Science research topic has been the development of tools and techniques to model, understand, and implement systems in which interaction is the norm

Some Views of the Field

Agents as a tool for understanding human

societies: Multiagent systems provide a novel new tool for simulating societies, which may help shed some light on various kinds of social processes.

This has analogies with the interest in

“theories of the mind” explored by some artificial intelligence researchers

Some Views of the Field

Multiagent Systems is primarily a search for

appropriate theoretical foundations: We want to build systems of interacting, autonomous agents, but we don’t yet know what these systems should look like

You can take a “neat” or “scruffy” approach to

the problem, seeing it as a problem of theory

• r a problem of engineering

This, too, has analogies with artificial

intelligence research

Objections to MAS

Isn’t it all just Distributed/Concurrent Systems?

There is much to learn from this community, but:

Agents are assumed to be autonomous,

capable of making independent decision – so they need mechanisms to synchronize and coordinate their activities at run time

Agents are (can be) self-interested, so their

interactions are “economic” encounters

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Objections to MAS

Isn’t it all just AI? We don’t need to solve all the problems of

artificial intelligence (i.e., all the components

• f intelligence) in order to build really useful

agents

Classical AI ignored social aspects of

• agency. These are important parts of

intelligent activity in real-world settings

Objections to MAS

Isn’t it all just Economics/Game Theory?

These fields also have a lot to teach us in multiagent systems, but:

Insofar as game theory provides descriptive

concepts, it doesn’t always tell us how to compute solutions; we’re concerned with computational, resource-bounded agents

Some assumptions in economics/game

theory (such as a rational agent) may not be valid or useful in building artificial agents

Objections to MAS

Isn’t it all just Social Science? We can draw insights from the study of

human societies, but there is no particular reason to believe that artificial societies will be constructed in the same way

Again, we have inspiration and cross-

fertilization, but hardly subsumption